Filter
  • Zeitbezug (75)
    -
  • Frequenz (124)
  • Kategorien
  • Datenanbieter
  • Art des Zugangs
  • Lizenzen
Alles zurücksetzen

150 Datensätze

Luft- und Raumfahrt

Alles zurücksetzen

WCS Anteil des amtl. festgesetzten Überschwemmungsgebietes mit bebauter Siedlungs- u. Verkehrsfläche

Der Indikator beschreibt die Siedlungslast im Überschwemmungsgebiet einer Gebietseinheit. Weitere Informationen unter http://www.ioer-monitor.de/index.php?id=44&ID_IND=R04RT. Für die Nutzung von WCS- und WFS-Diensten ist eine Registrierung nötig. Bitte melden Sie sich unter https://monitor.ioer.de/monitor_api/signup an.

Straßen
Bahn
Luft- und Raumfahrt
Infrastruktur
Bereitgestellt durch

Leibniz-Institut für ökologische Raumentwicklung

Art des Datenzugangs

Unbekannt / WMS

Aktualität der Datensatzbeschreibung

04.11.2021

Aktualisierungsfrequenz

Jährlich

Raumbezug

WMS Anteil baulich geprägter Siedlungs- und Verkehrsfläche an Gebietsfläche

Der Indikator beschreibt den Anteil des baulich geprägten Siedlungs- und des Verkehrsraumes in einer Gebietseinheit. Er korreliert positiv mit dem Versiegelungsgrad und negativ mit dem Freiraumanteil. Weitere Informationen unter http://www.ioer-monitor.de/index.php?id=44&ID_IND=S12RG

Straßen
Bahn
Luft- und Raumfahrt
Infrastruktur
Bereitgestellt durch

Leibniz-Institut für ökologische Raumentwicklung

Art des Datenzugangs

Unbekannt / WMS

Aktualität der Datensatzbeschreibung

04.11.2021

Aktualisierungsfrequenz

Jährlich

Raumbezug

Change Detection map of Germany 2016-2019 based on Sentinel-2 data

This change map was produced as an intermediate result in the course of the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data.

The map indicates land cover changes between the years 2016 and 2019. It is a difference map from two classifications based on Sentinel-2 MAJA data (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). More information on the two basis classifications can be found here:

https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/db130a09-fc2e-421d-95e2-1575e7c4b45c
https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/36512b46-f3aa-4aa4-8281-7584ec46c813

To keep only significant changes in the change detection map, the following postprocessing steps are applied to the initial difference raster:
- Modefilter (3x3) to eliminate isolated pixels and edge effects
- Information gain in a 4x4 window compares class distribution within the window from the two timesteps. High values indicate that the class distribution in the window has changed, and thus a change is likely. Gain ranges from 0 to 1, all changes < 0.5 are omitted.
- Change areas < 1ha are removed

The resulting map has the following nomenclature:
0: No Change
1: Change from low vegetation to forest
2: Change from water to forest
3: Change from built-up to forest
4: Change from bare soil to forest
5: Change from agriculture to forest
6: Change from forest to low vegetation
7: Change from water to low vegetation
8: Change from built-up to low vegetation
9: Change from bare soil to low vegetation
10: Change from agriculture to low vegetation
11: Change from forest to water
12: Change from low vegetation to water
13: Change from built-up to water
14: Change from bare soil to water
15: Change from agriculture to water
16: Change from forest to built-up
17: Change from low vegetation to built-up
18: Change from water to built-up
19: Change from bare soil to built-up
20: Change from agriculture to built-up
21: Change from forest to bare soil
22: Change from low vegetation to bare soil
23: Change from water to bare soil
24: Change from built-up to bare soil
25: Change from agriculture to bare soil
26: Change from forest to agriculture
27: Change from low vegetation to agriculture
28: Change from water to agriculture
29: Change from built-up to agriculture
30: Change from bare soil to agriculture

- Contains modified Copernicus Sentinel data (2016/2019), processed by mundialis

Incora report with details on methods and results: pending

mFUND-Projekt: incora, FKZ: 19F2079C

Luft- und Raumfahrt
Bereitgestellt durch

mundialis GmbH & Co. KG

Art des Datenzugangs

Dateidownload

Aktualität der Datensatzbeschreibung

25.10.2021

Zeitbezug der Daten

01.01.2016 — 31.12.2019

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

Landcover classification map of Germany 2020 based on Sentinel-2 data

This landcover map was produced with a classification method developed in the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data.

This classification is based on a time-series of monthly averaged, atmospherically corrected Sentinel-2 tiles (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). It consists of the following landcover classes:
10: forest
20: low vegetation
30: water
40: built-up
50: bare soil
60: agriculture

Potential training and validation areas were automatically extracted using spectral indices and their temporal variability from the Sentinel-2 data itself as well as the following auxiliary datasets:
- OpenStreetMap (Map data copyrighted OpenStreetMap contributors and available from htttps://www.openstreetmap.org)
- Copernicus HRL Imperviousness Status Map 2018 (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA))
- S2GLC Land Cover Map of Europe 2017 (Malinowski et al. 2020: Automated Production of Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523)
- Germany NUTS administrative areas 1:250000 (© GeoBasis-DE / BKG 2020 / dl-de/by-2-0 / https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html)
- Contains modified Copernicus Sentinel data (2020), processed by mundialis

Processing was performed for blocks of federal states and individual maps were mosaicked afterwards.
For each class 100,000 pixels from the potential training areas were extracted as training data.

An exemplary validation of the classification results was perfomed for the federal state of North Rhine-Westphalia as its open data policy allows for direct access to official data to be used as reference. Rules to convert relevant ATKIS Basis-DLM object classes to the incora nomenclature were defined. Subsequently, 5.000 reference points were randomly sampled and their classification in each case visually examined and, if necessary, revised to obtain a robust reference data set. The comparison of this reference data set with the incora classification yielded the following results:

overall accurary: 88.4%

class: user's accuracy / producer's accurary (number of reference points n)
forest: 95.0% / 93.8% (1410)
low vegetation: 73.4% / 86.5% (844)
water: 98.5% / 92.8% (69)
built-up: 98.9% / 95.8% (983)
bare soil: 23.9% / 82.9% (41)
agriculture: 94.6% / 83.2% (1653)

Incora report with details on methods and results: pending

mFUND-Projekt: incora, FKZ: 19F2079C

Luft- und Raumfahrt
Bereitgestellt durch

mundialis GmbH & Co. KG

Art des Datenzugangs

Dateidownload

Aktualität der Datensatzbeschreibung

25.10.2021

Zeitbezug der Daten

01.01.2020 — 31.12.2020

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

Landcover classification map of Germany 2019 based on Sentinel-2 data

This landcover map was produced as an intermediate result in the course of the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data.

This classification is based on a time-series of monthly averaged, atmospherically corrected Sentinel-2 tiles (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). It consists of the following landcover classes:
10: forest
20: low vegetation
30: water
40: built-up
50: bare soil
60: agriculture

Potential training and validation areas were automatically extracted using spectral indices and their temporal variability from the Sentinel-2 data itself as well as the following auxiliary datasets:
- OpenStreetMap (Map data copyrighted OpenStreetMap contributors and available from htttps://www.openstreetmap.org)
- Copernicus HRL Imperviousness Status Map 2018 (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA))
- S2GLC Land Cover Map of Europe 2017 (Malinowski et al. 2020: Automated Production of Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523)
- Germany NUTS administrative areas 1:250000 (© GeoBasis-DE / BKG 2020 / dl-de/by-2-0 / https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html)
- Contains modified Copernicus Sentinel data (2019), processed by mundialis

Processing was performed for blocks of federal states and individual maps were mosaicked afterwards.
For each class 100,000 pixels from the potential training areas were extracted as training data.

An exemplary validation of the classification results was perfomed for the federal state of North Rhine-Westphalia as its open data policy allows for direct access to official data to be used as reference. Rules to convert relevant ATKIS Basis-DLM object classes to the incora nomenclature were defined. Subsequently, 5.000 reference points were randomly sampled and their classification in each case visually examined and, if necessary, revised to obtain a robust reference data set. The comparison of this reference data set with the incora classification yielded the following results:

overall accurary: 91.9%

class: user's accuracy / producer's accurary (number of reference points n)
forest: 98.1% / 95.9% (1410)
low vegetation: 76.4% / 91.5% (844)
water: 98.4% / 92.8% (69)
built-up: 99.2% / 97.4% (983)
bare soil: 35.1% / 95.1% (41)
agriculture: 95.9% / 85.3% (1653)

Incora report with details on methods and results: pending

mFUND-Projekt: incora, FKZ: 19F2079C

Luft- und Raumfahrt
Bereitgestellt durch

mundialis GmbH & Co. KG

Art des Datenzugangs

Dateidownload

Aktualität der Datensatzbeschreibung

25.10.2021

Zeitbezug der Daten

01.01.2019 — 31.12.2019

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

Landcover classification map of Germany 2016 based on Sentinel-2 data

This landcover map was produced as an intermediate result in the course of the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data.

This classification is based on a time-series of monthly averaged, atmospherically corrected Sentinel-2 tiles (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). It consists of the following landcover classes:
10: forest
20: low vegetation
30: water
40: built-up
50: bare soil
60: agriculture

Potential training and validation areas were automatically extracted using spectral indices and their temporal variability from the Sentinel-2 data itself as well as the following auxiliary datasets:
- OpenStreetMap (Map data copyrighted OpenStreetMap contributors and available from htttps://www.openstreetmap.org)
- Copernicus HRL Imperviousness Status Map 2018 (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA))
- S2GLC Land Cover Map of Europe 2017 (Malinowski et al. 2020: Automated Production of Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523)
- Germany NUTS administrative areas 1:250000 (© GeoBasis-DE / BKG 2020 / dl-de/by-2-0 / https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html)
- Contains modified Copernicus Sentinel data (2016), processed by mundialis

Processing was performed for blocks of federal states and individual maps were mosaicked afterwards.
For each class 100,000 pixels from the potential training areas were extracted as training data.

An exemplary validation of the classification results was perfomed for the federal state of North Rhine-Westphalia as its open data policy allows for direct access to official data to be used as reference. Rules to convert relevant ATKIS Basis-DLM object classes to the incora nomenclature were defined. Subsequently, 5.000 reference points were randomly sampled and their classification in each case visually examined and, if necessary, revised to obtain a robust reference data set. The comparison of this reference data set with the incora classification yielded the following results:

overall accurary: 88.4%

class: user's accuracy / producer's accurary (number of reference points n)
forest: 96.7% / 94.3% (1410)
low vegetation: 70.6% / 84.0% (844)
water: 98.5% / 94.2% (69)
built-up: 98.2% / 89.8% (983)
bare soil: 19.7% / 58.5% (41)
agriculture: 91.7% / 85.3% (1653)

Incora report with details on methods and results: pending

mFUND-Projekt: incora, FKZ: 19F2079C

Luft- und Raumfahrt
Bereitgestellt durch

mundialis GmbH & Co. KG

Art des Datenzugangs

Dateidownload

Aktualität der Datensatzbeschreibung

25.10.2021

Zeitbezug der Daten

01.01.2016 — 31.12.2016

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

Change Detection map of Germany 2019-2020 based on Sentinel-2 data

This change map was produced on the basis of a classification method developed in the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data.

The map indicates land cover changes between the years 2019 and 2020. It is a difference map from two classifications based on Sentinel-2 MAJA data (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). More information on the two basis classifications can be found here:

https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/36512b46-f3aa-4aa4-8281-7584ec46c813
https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/9246503f-6adf-460b-a31e-73a649182d07

To keep only significant changes in the change detection map, the following postprocessing steps are applied to the initial difference raster:
- Modefilter (3x3) to eliminate isolated pixels and edge effects
- Information gain in a 4x4 window compares class distribution within the window from the two timesteps. High values indicate that the class distribution in the window has changed, and thus a change is likely. Gain ranges from 0 to 1, all changes < 0.5 are omitted.
- Change areas < 1ha are removed

The resulting map has the following nomenclature:
0: No Change
1: Change from low vegetation to forest
2: Change from water to forest
3: Change from built-up to forest
4: Change from bare soil to forest
5: Change from agriculture to forest
6: Change from forest to low vegetation
7: Change from water to low vegetation
8: Change from built-up to low vegetation
9: Change from bare soil to low vegetation
10: Change from agriculture to low vegetation
11: Change from forest to water
12: Change from low vegetation to water
13: Change from built-up to water
14: Change from bare soil to water
15: Change from agriculture to water
16: Change from forest to built-up
17: Change from low vegetation to built-up
18: Change from water to built-up
19: Change from bare soil to built-up
20: Change from agriculture to built-up
21: Change from forest to bare soil
22: Change from low vegetation to bare soil
23: Change from water to bare soil
24: Change from built-up to bare soil
25: Change from agriculture to bare soil
26: Change from forest to agriculture
27: Change from low vegetation to agriculture
28: Change from water to agriculture
29: Change from built-up to agriculture
30: Change from bare soil to agriculture

- Contains modified Copernicus Sentinel data (2019/2020), processed by mundialis

Incora report with details on methods and results: pending

mFUND-Projekt: incora, FKZ: 19F2079C

Luft- und Raumfahrt
Bereitgestellt durch

mundialis GmbH & Co. KG

Art des Datenzugangs

Dateidownload

Aktualität der Datensatzbeschreibung

25.10.2021

Zeitbezug der Daten

01.01.2019 — 31.12.2020

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

Existierende Bodennutzung 2015 im INSPIRE-Datenmodell - [WMS]

Dargestellt ist die tatsächliche Nutzung auf der Ebene der statistischen Blöcke des Regionalen Bezugssystems (RBS) sowie für Teilblöcke und Straßenflächen des Informationssystems Stadt und Umwelt (ISU). Sie sind durch Attribute des INSPIRE-Datenmodells "Bodennutzung" beschrieben.

Infrastruktur
Bahn
Luft- und Raumfahrt
Wasserstraßen und Gewässer
Straßen
Bereitgestellt durch

OpenData Berlin: Senatsverwaltung für Stadtentwicklung und Wohnen Berlin

Art des Datenzugangs

HTML / WMS

Aktualität der Datensatzbeschreibung

08.10.2021

Aktualisierungsfrequenz

Niemals

Raumbezug

Strat. Lärmkarte L_N (Nacht-Index) Flugverkehr 2012 (Umweltatlas) - [WMS]

Die Strategischen Lärmkarten 2012 geben Auskunft über die Lärmbelastung im Einwirkbereich von Hauptlärmquellen. Sie sind eine Fortführung der Strategischen Lärmkarten 2007.

Luft- und Raumfahrt
Bereitgestellt durch

OpenData Berlin: Senatsverwaltung für Umwelt, Verkehr und Klimaschutz Berlin

Art des Datenzugangs

Unbekannt / HTML / WMS

Aktualität der Datensatzbeschreibung

08.10.2021

Aktualisierungsfrequenz

Niemals

Raumbezug

Strat. Lärmkarte L_N (Nacht-Index) Flugverkehr BER Prognose 2015 (Umweltatlas) - [WMS]

Die Strategischen Lärmkarten 2012 geben Auskunft über die bestehende Lärmbelastung im Einwirkbereich von Hauptlärmquellen. Für den Bereich des zukünftigen Flughafens BER wird auch der prognostizierte Flugverkehr 2015 berücksichtigt.

Luft- und Raumfahrt
Bereitgestellt durch

OpenData Berlin: Senatsverwaltung für Umwelt, Verkehr und Klimaschutz Berlin

Art des Datenzugangs

Unbekannt / HTML / WMS

Aktualität der Datensatzbeschreibung

08.10.2021

Aktualisierungsfrequenz

Niemals

Raumbezug

81 - 90 von 150 Datensätzen

Ergebnisse als CSV oder RDF/XML